International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1195
The Effects of Beta-I and Fractal Dimension Neurofeedback on Reaction Time
Статья научная
In this paper, we evaluate the effects of neurofeedback training protocols of the relative power of the beta-I band and the fractal dimension on the reaction time of human by the Test of Variables of Attention (TOVA) to show which of these two protocols have the great ability for the improving of the reaction time. The findings of this research show that both protocols have a good ability (p < 0.01) to improving of the reaction time and can create the significant difference (as mean dRT = 37.3 ms for the beta-I protocol and dRT = 19.6 ms for the fractal protocol) in the reaction time. Of course, we must express, the Beta-I protocol has the more ability to improving of the reaction time and it is able to provide a faster reaction time.
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Статья научная
Recommender Systems (RS) help users in making appropriate decisions. In the area of RS research, many researchers focused on improving the performances of the existing methods, but most of them have not considered the potential of their employed methods in reaching the ultimate solution. In our view, the Machine Learning supervised approach as one of the existing techniques to create an RS can reach higher degrees of success in this field. Thus, we implemented a Collaborative Filtering recommender system using various Machine Learning supervised classifiers to study their performances. These classifiers implemented not only on a traditional platform but also on the Apache Spark platforms. The Caret package is used to implement the algorithms in the classical computational platform, and the H2O and Sparklyr are used to run the algorithms on the Spark Machine. Accordingly, we compared the performance of our algorithms with each other and with other algorithms from recent literature. Our experiments indicate the Caret-based algorithms are significantly slower than the Sparklyr and H2O based algorithms. Also, in the Spark platform, the runtime of the Sparklyr-based algorithm decreases with increasing the cluster size. However, the H2O-based algorithms run slower with increasing the cluster size. Moreover, the comparison of the results of our implemented algorithms with each other and with other algorithms from recent literature shows the Bayesian network is the fastest classifier between our implemented classifiers, and the Gradient Boost Model is the most accurate algorithm in our research. Therefore, the supervised approach is better than the other methods to create a collaborative filtering recommender system.
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Статья научная
There is a smoothing reactor and DC filter between the inverter and the direct current line to form a boundary in the HVDC transmission system. Since this boundary presents the stop-band characteristic to the high frequency transient voltage signals, the high-frequency transient voltage signal caused by external faults through boundary will be attenuated and the signals caused by internal faults will be unchanged. The wavelet analysis can be used as a tool to extract the feature of the fault to classify the internal fault and the external fault in HVDC transmission system. This paper explores the new method of wavelet based Multi-Resolution Analysis for signal decomposition to classify the difference types fault.
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Статья научная
In this paper, we present a new investigation to the literature, where we study the impact of false negative (FN) cost on the performance of cost sensitive learning. The proposed investigation approach has been performed on cost sensitive classifiers developed using Bayes minimum risk as an example of an applied mechanism for making a classifier cost sensitive. We consider a case study in credit card fraud detection, where FN refers to the number of fraudulent transactions that are miss-detected and approved as legitimate ones, assuming the classifier predicts the fraudulent transaction. Our investigation approach relies on testing the performance of various complex cost sensitive classifiers from different categories developed using Bayes minimum risk at different costs of FN. Our results show that those classifiers behave differently at different costs of FN including the real and average amount of transaction, and a range of random constant costs that are greater or less than the average amount. However, in general the results show that the lower the costs of FN are, the better the classifier performances are. This leads to different conclusions from the one drawn in [1], which states that choosing the cost of FN to be equal to the amount of transaction leads to better performance of cost sensitive learning using Bayes minimum risk. The results of this paper are based on the real life anonymous and imbalanced UCSD transactional data set.
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The Impact of Feature Selection on Web Spam Detection
Статья научная
Search engine is one of the most important tools for managing the massive amount of distributed web content. Web spamming tries to deceive search engines to rank some pages higher than they deserve. Many methods have been proposed to combat web spamming and to detect spam pages. One basic one is using classification, i.e., learning a classification model for classifying web pages to spam or non-spam. This work tries to select the best feature set for classification of web spam using imperialist competitive algorithm and genetic algorithm. Imperialist competitive algorithm is a novel optimization algorithm that is inspired by socio-political process of imperialism in the real world. Experiments are carried out on WEBSPAM-UK2007 data set, which show feature selection improves classification accuracy, and imperialist competitive algorithm outperforms GA.
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The Method of Measuring the Integration Degree of Countries on the Basis of International Relations
Статья научная
The paper studies the concept of integration, the integration of countries, basic characteristics of the integration of countries, the integration indicators of countries. The number of contacts between countries and the number of contracts signed between countries are offered as the indicators to determine the integration degree of countries. An approach to the design of the data warehouse for the decision support system in the field of foreign policy, using OLAP-technology is offered. Designed polycubic OLAP-model in which each cube is based on a separate data mart. Given the differences between the data warehouse and data mart. Shown that, one of the cubes of this model gives full information about the chosen indicators, including their aggregation on various parameters. Method for measuring the degree of integration of the countries, based on the calculation of the weight coefficients is proposed. In this regard, was described the information model of the relevant subsystem by using graph theory. Practical application of this method was shown. Moreover, the used software was shown.
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The Obstacle Detection and Measurement Based on Machine Vision
Статья научная
To develop a quick obstacle detection and measurement algorithm for the image-based autonomous vehicle (AV) or computer assisted driving system, this paper utilize the previous work of object detection to get the position of an obstacle and refocus windows on the selected target. Further calculation based on single camera will give the detailed measurement of the object, like the height, the distance to the vehicle, and possibly the width. It adopts a two camera system with different pitch angles, which can perform real-time monitoring for the front area of the vehicle with different coverage. This paper assumes that the vehicle will move at an even speed on a flat road, cameras will sample images at a given rate and the images will be analyzed simultaneously. Focus will be on the virtual window area of the image which is proved to be related to the distance to the object and speed of the vehicle. Counting of the blackened virtual sub-area can quickly find the existence of an obstacle and the obstacle area will be cut to get the interested parameter measurements for the object evaluation.
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The Research of Fuzzy Variable Transmission Ratio for Steer-by-wire System of Electric Forklift
Статья научная
Combining with the TE30 electric forklift produced by an enterprise, the principle of Steer-by-wire (SBW) system, steering motion state, ideal steering ratio are analyzed and studied. The biggest characteristic of SBW system is that the transmission ratio is free to design. Based on the establishment of two-degree-freedom linear model of Forklift, the paper designed the nonlinear transmission ratio function on vehicle speed and steering angle with the application of fuzzy control rules. The simulation results show that the fuzzy variable transmission ratio can make the yaw velocity gain tend to be constant, also can make Forklift light sensitive at low speeds and steady heavy at high speed. In order to ensure that the yaw velocity gain does not vary with the change of speed and steering angle, this paper presents a dynamic correction control strategy based on the steady-state control for Forklift. The simulation results show that the amplitudes of yaw velocity and sideslip angle are reduced with the dynamic correction of yaw velocity feedback, also the handling stability is improved.
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The Simulation Analysis of Nonlinear for a Power Amplifier with Memory Effects
Статья научная
For the nonlinear distortion problem of current power amplifiers (PAs) with memory effects, we use goal programming to present a memoryless predistorter matrix model based on limiting baseband predistortion technique, and the normalized mean squared error (NMSE) is limited in a satisfactory range while the output power is maximum. Then we propose a nonlinear power amplifier with memory effects based on back propagation neural network (BPNN) with three tapped delay nodes and six single hidden layer nodes, which is single input - dual output. Simulation results show that the method proposed in this paper makes the experimental precision higher. Further, the linearization effect of power amplifiers becomes better.
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The Study of Joint Acoustic Holography Algorithms based on Continuous Scanning
Статья научная
To effectively solve the problem of rapid measurement and recognition about large underwater sound source, continuous scanning is applied to measure the large underwater sound source. The theory of sound source recognition based on mobile framework technology (FAH)nd Helmholtz equation least squares method (HELS)s investigated. Combination of acoustic holography method based on MFAH and HELS is created and verified through simulation and basin test. The study shows that combination algorithm can accurately identify all kinds of underwater source and obtain a high positioning accuracy of the noise source, and can be used for a wide frequency range; when there are multiple coherent sound sources in the complex sound field, noise source identification and location only requires that an array holographic measurement surface is 1.3 times for the reconstruction surface. Using a small measuring surface to quickly identify large underwater sound source is achieved. The shortcomings of workload and time-consuming in the traditional measurement are resolved. And it provides convenience for engineering applications.
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Статья научная
This document presents two developed methods for solving the classification task of medical implant materials based on the compatible use of the Wiener Polynomial and SVM. The high accuracy of the proposed methodology for solving this task are experimentally confirmed. A comparison of the proposed methods with existing ones: Logistic Regression; Linear SVC; Random Forest; SVC (linear kernel); SVC (RBF kernel); Random Forest + Wiener Polynomial is carried out. The duration of training of all methods that described in work is investigated. The article presents the visualization of all method results for solving this task.
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The decision model for selection of tourism site using Analytic Network Process method
Статья научная
The criteria and sub criteria-based decision model for selection of tourism site using Analytic Network Process (ANP) method was to be implemented in Yogyakarta, Indonesia. In this study, we proposed criteria and sub criteria that influenced each other and had feedback between the two so that there was a comparison of tourism site alternatives according to sub criteria and pairwise comparative assessment with scale 1-9 that was then calculated in form of matrix of pairwise comparison. The result of this study was in form of decision alternatives in form of ranking to facilitate decision makers (DMs) in finding tourism sites.
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The information support of virtual research teams by means of cloud managers
Статья научная
The article deals with the creation of virtual research teams of scientists from various geographically distributed organizations united for joint interdisciplinary researches. Library social institutions are the satellites of virtual research teams and have to implement information and communication support of scientific researches. The use of cloud managers by academic libraries is proposed as platforms to facilitate remote collaborative work of the participants of the virtual research teams. The research of number of free cloud managers and their capabilities was held. The most successful cloud manager for supporting the scientific work of virtual research teams was selected by using hierarchy analysis method.
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Статья научная
The subject matter of the article is developing information and communication network (ICN) for critical infrastructure systems (CIS). The aim of the work is to provide high-quality information and telecommunication processes by developing the optimal version of distributing CIS functional tasks and ICN processes to the network nodes. The article deals with following problems: developing a model for mapping the information and technical ICN structures, developing a method for variant synthesis of ITS structural models, a formalized representation of the problem of selecting CIS optimal structure. The methods used are: the system method, the set-theoretic and graphic analytic approaches, methods of hierarchic structures synthesis, optimization methods. The following results were obtained: the use of system approach for formalizing the information processing process in CIS was justified; mapping the ICS functional system into the information and technical one was presented as multilevel graph chain; the generalized representation of graph structures hierarchy was developed for the set of data transmitting tasks; this approach enabled formal representing alternative variants that consider the main links, sequencing, the amount and flows of the processed information among the different structure levels; the scheme of variant synthesis method of ICN models according to graph structures mapping was developed; the problem of selecting optimal ICN structures was formally presented; a complex efficiency criterion for solving problems of optimizing variant synthesis of structures; the problem of optimal synthesis of the structure of the given level factored in resource constraints was formulated. Conclusions. The article deals with such novelty aspects as improving the model of problem of selecting the optimal ICN structure by set-theoretic formalization factored in the criterion of maximum intensity of computational resource application, which enabled determining structural links among the major elements considering the decomposition of the model up to the basic elements such as "node" and "task" and the development of a new method of optimal ICN structuring which unlike the existing ones involves the variant synthesis of structures hierarchy and formalizing selection problems on the basis of set-theoretic models, which enables providing the efficiency of application of information and technical net resources.
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The neurocontroller for satellite rotation
Статья научная
In this work an analysis of neurocontrollers is given. The purpose of this paper is the neurocontroler for attitude control: satellite rotations. The classification of neurocontroller architecture is provided. The pros and cons of different neurocontrollers are described. Two configuration of neural network – feedforward neural networks with mini-batch descent and modified Elman neural network, are investigated in this work to verify its ability to control the attitude of a satellite. The advantages and disadvantage of different predictive model neurorization systems are described. The class diagram for the simulating of satellite rotation for neural network learning is given. The proposed approach provides the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. The accuracy was calculated. The AI module, after trained for different configurations of wheels, will get commands with desired 3D rotation speeds and control the wheels to achieve the desired rotation speeds.
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Theoretical Design and Computational Fluid Dynamic Analysis of Projectile Intake
Статья научная
With the development of the science and technology, the more requirements such as cost effective, high specific impulse in wide operation rang, becomes stricter and multiplicity. However, the existing supersonic inlet can no longer adjust to all the new projectiles. In this paper, based on the basic characteristic of inlet and considering the design requirements, the two-dimensional supersonic projectile inlet was designed and verified by numerical simulation under different operating conditions such as attack angle, altitude, and so on. The results are shown that: 1) The design process is successful, but the working conditions should be limited to the small angle of attack; 2) The total pressure recovery coefficient is increasing as the Ma number increases, and then is gradually decreased after the point of Mach number is equal to 0.5; 3) The existence of attack angle reduces values of total pressure recovery. And moreover, the shock wave which occurs at the anterior point is gradually deviating from projectile body direction with the increase of attack angle; 4). The variance ratio in the outlet has the acute changed with increasing of altitudes clearly, but its corresponding values degrade sharply in the entrance.
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Threshold controlled binary particle swarm optimization for high dimensional feature selection
Статья научная
Dimensionality reduction or the optimal selection of features is a challenging task due to large search space. Currently, many research has been performed in this domain to improve the accuracy as well as to minimize the computational complexity. Particle Swarm Optimization (PSO) based feature selection approach seems very promising and has been extensively used for this work. In this paper, a Threshold Controlled Binary Particle Swarm Optimization (TC-BPSO) along with Multi-Class Support Vector Machine (MC-SVM) is proposed and compared with Conventional Binary Particle Swarm Optimization (C-BPSO). TC-BPSO is used for the selection of features while MC-SVM is used to calculate the classification accuracy. 70% of the data is used to train the MC-SVM model while the test has been performed on rest 30% data to calculate the accuracy. Proposed approach is tested on ten different datasets having varying difficulties such as some datasets having large number of features while some have small, some have just two classes while some have many classes, some datasets having small number of instances while some have large number of instances and the results obtained on these datasets are compared with some of the existing methods. Experiments show that the obtained results are very promising and achieved the best accuracy in minimum possible features. Proposed approach outperforms C-BPSO in all contexts on most of the datasets and 3-4 times computationally faster. It also outperforms in all context when compared with the existing work and 5-8 times computationally faster.
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Time Series Forecasting Model Based on Discrete Grey LS-SVM
Статья научная
The advantages and disadvantages of discrete GM(1,1) model and least squares support vector machine are analyzed respectively, this article proposes a new time series forecasting model of discrete grey least squares support vector machine. The new model adopts structural risk minimization principle, at the same time develops the advantages of accumulation generation in the grey forecasting method, weakens the effect of stochastic-disturbing factors in original sequence, and avoids the theoretical defects existing in the grey forecasting model. The simulation results show that the forecasting model is effective and reliable, and consolidates the advantage of the discrete GM(1,1) model and least squares support vector machine. It offers a new way to improve the time series forecasting accuracy.
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Time Synchronization under 1PPS Signal in Distributed Real-time Simulation System
Статья научная
Time synchronization is one of the key points in distributed of a real-time simulation system. At first, a distributed simulation system is introduced. Secondly, time synchronization method under 1PPS signal is put forth. Thirdly, the relevant technology of time synchronization are studied, including system structure, SNTP, 1PPS signal and control logic. Vxworks watch dog timer mode and timecard counter mode under this method are also analyzed in detail and the precision is presented. Fourthly, Arena software is chosen to model and simulate the time synchronization procedure. At last, the whole simulation model is constructed and the experiment results are given out to prove the efficiency of the synchronization method.
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Статья научная
Time-frequency and time dependence of the output signal morphology of nonlinear oscillator neuron based on Van der Pol model using analytical and numerical methods were investigated. Threshold effect neuron, when it is exposed to external non-stationary signals that vary in shape, frequency and amplitude was considered.
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